cudaBayesreg: Parallel Implementation of a Bayesian Multilevel Model for fMRI Data Analysis

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Abstract

Graphic processing units (GPUs) are rapidly gaining maturity as powerful general par-allel computing devices. A key feature in the development of modern GPUs has been theadvancement of the programming model and programming tools. Compute Unified De-vice Architecture (CUDA) is a software platform for massively parallel high-performancecomputing on Nvidia many-core GPUs. In functional magnetic resonance imaging (fMRI),the volume of the data to be processed, and the type of statistical analysis to perform callfor high-performance computing strategies. In this work, we present the main features ofthe R-CUDA package cudaBayesreg which implements in CUDA the core of a Bayesianmultilevel model for the analysis of brain fMRI data. The statistical model implementsa Gibbs sampler for multilevel/hierarchical linear models with a normal prior. The maincontribution for the increased performance comes from the use of separate threads forfitting the linear regression model at each voxel in parallel. The R-CUDA implementationof the Bayesian model proposed here has been able to reduce significantly the run-timeprocessing of Markov chain Monte Carlo (MCMC) simulations used in Bayesian fMRIdata analyses. Presently, cudaBayesreg is only configured for Linux systems with NvidiaCUDA support.
Original languageUnknown
Pages (from-to)1-24
JournalJournal Of Statistical Software
Volume44
Issue number4
Publication statusPublished - 1 Jan 2011

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